Information discovery system

ABSTRACT

Systems, device and techniques are disclosed for an information discovery system. An element of data may be retrieved. A knowledge point may be extracted from the element of data. The knowledge point may include an aspect of the element of data. The element of data and the knowledge point may be linked with a traversable link. The knowledge point may further be linked to a second element of data. Natural language processing analysis, linguistic analysis, sentiment analysis, and metadata analysis, may be used to determine the aspect of the element of data.

PRIORITY

This application claims the benefit of U.S. Provisional Application No.62/068,129, filed Oct. 24, 2014, the disclosure of which is incorporatedby reference in its entirety.

BACKGROUND

Items of information related to a topic may be scattered among manydifferent sources. The items of information and their sources may notall turn up in searches for certain keywords or search terms that areotherwise related to that topic, as the items of information may notinclude keywords related to the topic that a user might search whenattempting to find more items of information related to the topic. Thismay result in some sources and items of information that are related toa topic being difficult to find through direct keyword searching. Suchsources and items of information may be relevant to the topic beingsearched despite not including the keywords related to the topic.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are included to provide a furtherunderstanding of the disclosed subject matter, are incorporated in andconstitute a part of this specification. The drawings also illustrateimplementations of the disclosed subject matter and together with thedetailed description serve to explain the principles of implementationsof the disclosed subject matter. No attempt is made to show structuraldetails in more detail than may be necessary for a fundamentalunderstanding of the disclosed subject matter and various ways in whichit may be practiced.

FIG. 1 shows an example process for an information discovery system,according to an implementation of the disclosed subject matter.

FIG. 2 shows an example process for an information discovery system,according to an implementation of the disclosed subject matter.

FIG. 3 shows an example system for an information discovery systemaccording to an implementation of the disclosed subject matter.

FIG. 4 shows an example system for an information discovery systemaccording to an implementation of the disclosed subject matter.

FIG. 5 shows an example system for an information discovery systemaccording to an implementation of the disclosed subject matter.

FIG. 6 shows an example knowledge graph for an information discoverysystem according to an implementation of the disclosed subject matter.

FIG. 7 shows a computer according to an implementation of the disclosedsubject matter.

FIG. 8 shows a network configuration according to an implementation ofthe disclosed subject matter.

DETAILED DESCRIPTION

Techniques disclosed herein enable an information discovery system,which may allow for the discovery of items of information, or elementsof data, related to a particular topic from various sources that may notbe surfaced through direct keyword searching. The information discoverysystem may perform data acquisition, during which various elements ofdata from any suitable sources, such as, for example, posts from socialnetwork sites, may be examined. Knowledge points may be extracted fromthe elements of data, for example, by knowledge point extractors of theinformation discovery system. Knowledge points may include, for example,author and recipient information, source site information, and knowledgepoints including, for example, an industry associated with the elementsof data, sentiment expressed in the elements of data, and purchaseintent expressed by the elements of data. The determination of suchknowledge points may be accomplished by parsing content using NaturalLanguage Processing (NLP) techniques. The extracted knowledge points maybe stored by the information discovery system, which may be queryable.The extracted knowledge points may be stored with a link to the originalelement of data, or document, from which they were extracted, and aweighting that may indicate the relative strength of the link orconfidence level that the knowledge point represents some aspect of thelinked element of data. The links may provide traversable pathwaysbetween documents based on a wide variety of criteria.

The links between knowledge points and elements of data may create aknowledge graph in the information discovery system. The knowledge graphmay be traversed based on user defined queries. A user could, forexample, identify a knowledge point representing something of interestto the user, such as, for example, social media posts which contain aspecific keyword, such as a brand name the user manages. This initialset of seed knowledge points found in response to the criteria set bythe user may in turn contain links to a series of additional elements ofdata, such as documents, which in turn may relate to additionalknowledge points. The user may then identify those additional knowledgepoints which may be of relevance to the user, which would in turnfeedback additional sets of elements of data that may be related to theuser's initial search. Because links between elements of data may comefrom the extraction of semantically relevant knowledge points, thechance of encountering relevant data which is not surfaced throughdirect provision of keywords may be enhanced.

The information discovery system may gather elements of data from anysuitable source. For example, the elements of data may be posts made byusers to various social networking services, blogs, comment boards, andso on. The elements of data may be gathered based on keyword and imagesearches, or using bulk retrieval such as web crawling or aggregationfrom a third party service. The elements of data may be analyzed for allsuitable features, such as, for example, natural language processingbased features such as named entity or sentiment or custom featuredefinitions such as the existence of a URL within the element of data.The features may be knowledge points extracted from the elements ofdata.

The identified features, or knowledge points, may be used as connectionsto other graphs by establishing the knowledge points as nodes.Connections from the established nodes may allow for discovery fromsocial graphs, which may be based on the connections between people whopost to social networking services and may allow assertions aroundinfluence, Internet of things correlations and graphs, including theknowledge points position and relationship to other nodes that may bepart of the Internet of things, publicly available knowledge graphswhich may provide for relative distance between knowledge ontologies,their position in a taxonomy and relationships to other knowledgeconcepts, document clusters which may identify similar ideas, memes, andviral posts, and NLP extractions, such as phrases and named entityrecognition.

The way in which a user interfaces with results returned from searchingthrough information discovery system may be feedback for tuning throughexplicit utilization. For example, user feedback may be used to overridevalues like sentiment and category, manual categorization, to build orcustomize lexicons and dictionaries, and to correct any erroneous orotherwise incorrect knowledge points or graph connection.

The user feedback may also be implicit, for example, based onutilization of posts and connections through queries into the system,user engagement, and a social graph seeded by accounts owned by the useror accounts provided or identified by the user, such as accountsbelonging to competitors or influencers. These variables may be weightedand then fed back into the information discovery system, which may allowfor crawling into additional sources of data and for feed back to theuser of additional related elements of data.

According to implementations of the disclosed subject matter, as shownin FIG. 1, a knowledge graph may be constructed. As shown at step 101,elements of data may be retrieved. At step 102, knowledge points may beextracted from the elements of data. At step 103, the knowledge pointsand the elements of data may be linked.

According to implementations of the disclosed subject matter, at step101 in FIG. 1, elements of data may be retrieved. For example, theinformation discovery system may retrieve elements of data, such asdocuments or posts, from social networking services or any othersuitable locations and data sources. The information discovery systemmay use, for example, the social networking account of a user toretrieve the elements of data, or may be directed to other socialnetworking accounts of interest to the user and may retrieve elements ofdata which may be publicly available or may be available through anaccount of the user, or may crawl publicly available websites.

According to implementations of the disclosed subject matter, at step102, knowledge points may be extracted from the elements of data. Forexample, knowledge point extractors of the information discovery systemmay extract knowledge points from the retrieved elements of data. Theextracted knowledge points may be, for example, author and recipientinformation and other metadata, source site information, and key pointsof the elements of data as evaluated through NLP, sentiment, andlinguistic analysis, including industry, product, sentiment, andpurchase intent. These extracted knowledge points may be stored in theinformation discovery system.

According to implementations of the disclosed subject matter, at step103, the knowledge points and the elements of data may be linked. Forexample, the information discovery system may include links betweenknowledge points and the elements of data from which the knowledgepoints were extracted, with knowledge points that are common betweenelements of data serving as a link between the elements of data. Forexample, separate social media posts by the same author may be linked inthe information discovery system through the knowledge point of authorinformation for that author. The links may provide traversable pathwaysbetween elements of data based on a wide variety of criteria. The linksbetween elements of data and knowledge points may be weighted, forexample, with the weight representing a confidence level that theknowledge point accurately describes an aspect of the element of data.For example, the weight of a link between an element of data andknowledge point representing a specific author may represent a level ofconfidence that the author in the knowledge point is the author of theelement of data. An element of data may be linked to any number ofknowledge points, and a knowledge point may be linked to any number ofelements of data. The knowledge points and linked elements of data maybe stored in knowledge graphs. The knowledge graph may store copies ofthe elements of data or a link to the element of data at its originalsource. For example, the knowledge graph may include a social media postby storing a copy of the social media post, or by storing a copy of aURL or other locator that may be used to retrieve the social media postfrom the social media service on which it is hosted.

According to implementations of the disclosed subject matter, as shownin FIG. 2, a knowledge graph may be searched. As shown at step 201, auser criteria may be received. At step 202, knowledge points related tothe user criteria may be displayed. At step 203, knowledge points basedon user traversal of links from knowledge points related to usercriteria may be displayed.

According to implementations of the disclosed subject matter, at step201 in FIG. 2, a user criteria may be received. For example, theinformation discovery system may receive criteria from a user such as,for example, a keyword, phrase, search term, or natural languagequestion that may be of interest to the user. For example, the keywordmay be related to a brand managed by the user.

According to implementations of the disclosed subject matter, at step202, knowledge points related to the user criteria may be displayed. Forexample, the user criteria may be a keyword used to search the knowledgepoints stored in the knowledge graph of the information discoverysystem. Knowledge points which contain the keyword may be displayed tothe user. These knowledge points may form a set of seed points fromwhich the user may further explore the knowledge graph. The elements ofdata linked to the displayed knowledge points may also be displayed tothe user. The user may examine the elements of data, for example,viewing documents or social media posts as either copies or at theiroriginal source through URL's or other locators included in theknowledge graph.

According to implementations of the disclosed subject matter, at step203, knowledge points based on user traversal of links from knowledgepoints related to user criteria may be displayed. For example, the usermay traverse the links between elements of data based on links to theknowledge points that were displayed in response to the user criteria.This may result in the display of additional knowledge points as theuser traverse the knowledge graph, starting from the seed points. Thelinks may result in knowledge points being displayed to the user whichare relevant to the user's criteria but which were not surfaced in theinitial search due to, for example, not including the exact keyword theuser entered as criteria.

FIG. 3 shows an example system for an information discovery system. Asystem 300 may include an information discovery system 301, a userdevice 302, a computer network 303, and a data source 304. Only one userdevice 302 is depicted in FIG. 3, although the system 300 may have morethan one user device 302 operating at the same time.

The information discovery system 301 may be configured to retrieveelements of data from data sources such as the data source 304, extractknowledge points from the retrieved elements of data, create linkbetween elements of data and knowledge points to create a knowledgegraph, and host the knowledge graph so that it is accessible,searchable, and traversable by a user using the user device 302. Theinformation discovery system 301 may include knowledge point extractors,and may employ and suitable techniques such as NLP, or evaluating otherelements of an element of data to extract knowledge points from theelements of data retrieved from the data source 304.

The user device 302 may be configured to provide input or receive outputto and from the information discovery system 301 in order to carry outone or more of the steps 201, 202, and 203. The information discoverysystem 301 may include one or more server computers, computing devices,or other such computing systems. The information discovery system 301include any suitable software, hardware, and componentry, such as, forexample, microprocessors, memory systems, input/output devices, devicecontrollers and display systems. The information discovery system may bea single server, or may be number of servers or other computing deviseinterconnected by suitable hardware and software systems and whichcollectively can perform any suitable functions of the informationdiscovery system 301, such as, for example, the steps 101, 102, 103,201, 202, and 203. The user device 302 may include any type of devicecapable of accessing the information discovery system 301, for example,a smartphone, PDA, tablet, gaming system, personal computer, laptop, andcell phone.

The data source 304 may be any suitable source of elements of data forthe information discovery system 301. For example, the data source 304may be a social networking service hosted on any suitable server system,or a database of any kind that may include elements of data. The datasource 304 may be accessible to the information discovery system 301through the computer network 303, which may be, for example, theInternet.

Each component in the system 300 may communicate with otherelectronically coupled components through the network 303. The network303 may include, for example, the Internet, a WAN, LAN, private network,public network, or any other type of computer network. The communicationbetween any component and another computing device may be bidirectional.

FIG. 4 shows an example system for an information discovery systemaccording to an implementation of the disclosed subject matter. Theinformation discovery system 301 may be any suitable combination ofhardware and software, and may be implemented on any suitable number andarrangement of physical and virtual computing devices connected in anysuitable manner. For example, the information discovery system 301 maybe a single computer or server, may be server system or cluster, or maybe implemented in multiple server systems at separate physicallocations.

The information discovery system 301 may include an extractor 430 and aretriever 440, which may both be any suitable combinations of hardwareand software. The information discovery system 301 may store extractionplugins 450, which may be any suitable plugin that may be used by theextractor 430 to extract knowledge points from elements of data, such asdata elements 490. The information discovery system 301 may store theknowledge graph 460, which may include knowledge points and linked dataelements, in any suitable storage. The knowledge graph 460 may also bestored in a suitable storage separately from the information discoverysystem 301, for example, on a separate server or database system towhich the information discovery system 301 is connected using anysuitable network connection.

The information discovery system 301 may access the data elements 490from the data source 304 and may receive a data element, for example, asin step 101. The data source 304 may be any suitable repository of data,including, for example, websites and network services accessible usingany suitable network protocols, and may be public or internal. Forexample, the data source 304 may be a publicly accessible message board,a message board internal to a particular company's network, a newswebsite, a social network, a repository of product reviews on acommercial or review-hosting website, and so on. The data elements 490may be, for example, message board or social media postings, newsarticles, or product reviews. The data source 304 may be, for example, aCustomer Relationship Management (CRM) platform, which may gather socialmedia and other postings.

The extractor 430 may apply any suitable plugin from the extractionplugins 450 to the data element received from the data source 304 toextract knowledge points from the received data element, for example, asin step 102. The plugins may be for, for example, Natural LanguageProcessing (NLP) analysis including linguistic analysis and subjectanalysis, sentiment analysis including customer satisfaction analysisand intent to buy analysis, and metadata analysis. For example, a pluginmay be used by the extractor 430 to extract a knowledge point from thereceived data element regarding a product which is the subject of thedata element. Another plugin may be used by the extractor 430 to extracta knowledge point from the received data element regarding whether thedata element indicates an intent to buy the product which is the subjectof the data element.

The extractor 430 may store extracted knowledge points in any suitableformat. For example, a knowledge point may be stored as a key/valuepair. For example, a knowledge point may a key value of “SENTIMENT”,which may indicate the nature of the knowledge point. The value of theknowledge point may then be chosen from a set of values related tosentiment, such as “POSITIVE”, “NEGATIVE”, “NEUTRAL”, “UNKNOWN”.

The extractor 430 may build the knowledge graph 460 from data elementsand extracted knowledge points, and may extend the knowledge graph 460with additional data elements and extracted knowledge points. Theextractor 430 may link the knowledge points extracted from the dataelement to the data element in the knowledge graph 460, for example, asin step 103. For example, the extractor 430 may establish a link betweenthe extracted knowledge point of the product that is the subject of thedata element and the date element itself. This link may added to theknowledge graph 460. The extracted knowledge point may be stored in theknowledge graph 460, and may be linked either to a copy of the dataelement, or to some representation of the data element, such as, forexample, a URL at which the data element is located. The extractor 430may weight a link between a knowledge point and a data element. Forexample, if the extractor 430 used a plugin for sentiment analysis of adata element, there may be a confidence level attached to the knowledgepoint extracted from the data element. For example, the extractor 430may determine that there is a 75% chance that the data element indicatesan intent to buy a product that is the subject of the data element. Theintent to buy knowledge point may be linked to the data element in theknowledge graph 460, and the link may have a weighting of 75%. A higherweighting in a link between a knowledge point and data element mayindicate a higher confidence level that the knowledge point accuratelydescribes some aspect of the data element.

FIG. 5 shows an example system for an information discovery systemaccording to an implementation of the disclosed subject matter. The userdevice 302, which may be any suitable computing device such as, forexample, a smartphone, PDA, tablet, gaming system, personal computer,laptop, or cell phone, may be used to submit criteria to the informationdiscovery system 301, for example, as in step 201. The criteria may be,for example, a keyword, phrase, metadata item, or natural languagequestion indicating a subject for which the user of the user device 302wishes to view related data elements.

The retriever 440 may compare the criteria received from the user device302 to the knowledge points stored in the knowledge graph 460. Theretriever 440 may retrieve knowledge points matching the criteria fromthe knowledge graph 460 and send them to the user device 302 to bedisplayed to the user, as in step 202. A knowledge point may be matchedto the criteria based on, for example, phrase or keyword matching, orbased on analysis, for example, NLP, linguistic, or sentiment analysisof the criteria. For example, the criteria may be in the form of aquestion, and matching knowledge points may be knowledge points that maybe linked to data elements that answer the question. The criteria may bebased on metadata, for example, indicating the name of an author, andmay be matched to knowledge points extracted by a metadata extractingplugin.

Knowledge points may be displayed on the user device 302 using anysuitable graphical representation, including, for example, list or graphrepresentations. A user may be able to examine a knowledge pointretrieved from the knowledge graph 460 to view the data elements linkedto that knowledge point. Data elements, such as the data elements 490,may be viewed at their original source, such as the data source 304, orwhere a copy is hosted, such as in the knowledge graph 460. For example,a knowledge point may be linked to an article hosted on a website, andto a social media post. The user, with the user device 302, may be ableto view the article and the social media post, either through copiessent from knowledge graph 460 to the user device 302 with the knowledgepoints or on selection of the data elements by the user, or at theiroriginal sources, for example, through URL's or other link to thewebsite that hosts the article and social media service that hosts thepost.

The user may, using the user device 302, traverse the links from theknowledge points that were related to the criteria and sent from theinformation discovery system 301 to the user device 302, as in step 203.Each knowledge point may be linked to multiple data elements. The usermay select a data element linked to a knowledge point sent to the userdevice 302 based on the criteria that were sent from the user device302. The user may then be presented with additional knowledge pointsthat are linked to that selected data element, although these additionalknowledge points may not match the criteria that were sent from the userdevice 302. The additional knowledge points may be linked to additionaldata elements, which the user may examine, and may select to view evenmore additional linked knowledge points. Whenever the user selects adata element, the retriever 440 may retrieve the additional knowledgepoints linked to the selected data element from the knowledge graph 460and send the additional knowledge points, and additional linked dataelements linked to these additional knowledge points, to the user device302. This may allow a user to explore the knowledge graph 460 based ontheir originally sent criteria, and traversing links between knowledgepoints and data elements.

FIG. 6 shows an example knowledge graph for an information discoverysystem according to an implementation of the disclosed subject matter.Data elements, such as the data elements 601, 602, 603, 604, and 605,may be linked to knowledge points, such as the knowledge points 611,612, and 613, in the knowledge graph 460. Each of the of the knowledgepoints 611, 612, and 613, may represent some aspect of a data element,and data elements linked to particular knowledge point may share thoseaspects. The knowledge point 611 may represent, for example, a specificauthor. Data elements linked to the knowledge point 611 may be thosedata elements that were determined, for example, through extraction ofmetadata or linguistic analysis by the extractor 430, to have beenwritten by that specific author. Thus, the data elements 601 and 603 maybe determined to share a common author, the author represented in theknowledge point 611. The knowledge point 613 may represent, for example,a specific product. Data elements linked to the knowledge point 613, forexample, the data elements 604 and 605, may be data elements that weredetermined by the extractor 430 to be about or pertain to the specificproduct represented by the knowledge point 613.

A user may traverse the knowledge graph 460 across the links betweendata elements and knowledge points. For example, the criteria receivedfrom user device may be the name of a specific author, which may be theauthor represented by the knowledge point 611. The user device 302 mayreceive, from the information discovery device 301, the knowledge point611, which may match the criteria of author name received from the userdevice 302, and linked data elements 601 and 603. The user may examinethe data elements 601 and 603, for example, viewing copies of them orbeing directed to the original versions on, for example, a website orsocial media service. The user may also, upon examining the dataelements 601 and 603, be presented with the linked knowledge point 612.The user may indicate that they wish to view data elements connected tothe knowledge point 612, representing a sentiment, and the retriever 440may send to the user device 302 the data elements 602 and 604 which arelinked to the knowledge point 612. The user may then examine the dataelements 602 and 604, as well as the knowledge point 613. The user maythe use the knowledge point 613 to receive and view the data element605. In this way, a user may traverse the knowledge graph 460, and mayview data elements that may not turn up based on the initial criteriasearched for by the user but might still be relevant to the user'sinterests. This may allow for the surfacing of relevant data elementsbeyond the boundaries of results provided by searching on a keyword orphrase.

Each link between a data element and a knowledge point may be weightedto represent a confidence level that the knowledge point accuratelydescribes some aspect of the linked data element. For example, theknowledge point 612 may be a sentiment extracted from the linked dataelements 601, 602, 603, and 604, for example, by the extractor 430. Thesentiment may be, for example, whether the data elements 601, 602, 603,and 604 indicate satisfaction with some product or service which is thesubject of one of the data elements 601, 602, 603, and 604. The productor service may be different for the different data elements, but thesentiment of the knowledge point 612 may be the same, as a data elementmay only be linked to the knowledge point 612 if the data elementincludes the sentiment of the knowledge point 612.

The weighting of 95% of the link between the data element 602 and theknowledge point 612 may indicate a 95% confidence that the data element602 includes the sentiment of the knowledge point 612. For example, thedata element 602 may be a blog post about a product which the extractor430 has determined with 95% confidence indicates customer satisfactionwith the product. The weighting of 50% of the link between the dataelement 603 and the knowledge point 612 may indicate a 50% confidencethat the data element 603 includes the sentiment of the knowledge point612. For example, the data element 603 may be a website article about aproduct which may contain some ambiguity which may not be resolvedentirely by the extractor 430 using any suitable sentiment analysis.

Implementations of the presently disclosed subject matter may beimplemented in and used with a variety of component and networkarchitectures. FIG. 7 is an example computer 20 suitable forimplementing implementations of the presently disclosed subject matter.As discussed in further detail herein, the computer 20 may be a singlecomputer in a network of multiple computers. As shown in FIG. 7,computer may communicate a central component 30 (e.g., server, cloudserver, database, etc.). The central component 30 may communicate withone or more other computers such as the second computer 31. According tothis implementation, the information obtained to and/or from a centralcomponent 30 may be isolated for each computer such that computer 20 maynot share information with computer 31. Alternatively or in addition,computer 20 may communicate directly with the second computer 31.

The computer (e.g., user computer, enterprise computer, etc.) 20includes a bus 21 which interconnects major components of the computer20, such as a central processor 24, a memory 27 (typically RAM, butwhich may also include ROM, flash RAM, or the like), an input/outputcontroller 28, a user display 22, such as a display or touch screen viaa display adapter, a user input interface 26, which may include one ormore controllers and associated user input or devices such as akeyboard, mouse, WiFi/cellular radios, touchscreen, microphone/speakersand the like, and may be closely coupled to the I/O controller 28, fixedstorage 23, such as a hard drive, flash storage, Fibre Channel network,SAN device, SCSI device, and the like, and a removable media component25 operative to control and receive an optical disk, flash drive, andthe like.

The bus 21 enable data communication between the central processor 24and the memory 27, which may include read-only memory (ROM) or flashmemory (neither shown), and random access memory (RAM) (not shown), aspreviously noted. The RAM can include the main memory into which theoperating system and application programs are loaded. The ROM or flashmemory can contain, among other code, the Basic Input-Output system(BIOS) which controls basic hardware operation such as the interactionwith peripheral components. Applications resident with the computer 20can be stored on and accessed via a computer readable medium, such as ahard disk drive (e.g., fixed storage 23), an optical drive, floppy disk,or other storage medium 25.

The fixed storage 23 may be integral with the computer 20 or may beseparate and accessed through other interfaces. A network interface 29may provide a direct connection to a remote server via a telephone link,to the Internet via an internet service provider (ISP), or a directconnection to a remote server via a direct network link to the Internetvia a POP (point of presence) or other technique. The network interface29 may provide such connection using wireless techniques, includingdigital cellular telephone connection, Cellular Digital Packet Data(CDPD) connection, digital satellite data connection or the like. Forexample, the network interface 29 may enable the computer to communicatewith other computers via one or more local, wide-area, or othernetworks, as shown in FIG. 8.

Many other devices or components (not shown) may be connected in asimilar manner (e.g., document scanners, digital cameras and so on).Conversely, all of the components shown in FIG. 7 need not be present topractice the present disclosure. The components can be interconnected indifferent ways from that shown. The operation of a computer such as thatshown in FIG. 7 is readily known in the art and is not discussed indetail in this application. Code to implement the present disclosure canbe stored in computer-readable storage media such as one or more of thememory 27, fixed storage 23, removable media 25, or on a remote storagelocation.

FIG. 8 shows an example network arrangement according to animplementation of the disclosed subject matter. One or more clients 10,11, such as computers, microcomputers, local computers, smart phones,tablet computing devices, enterprise devices, and the like may connectto other devices via one or more networks 7 (e.g., a power distributionnetwork). The network may be a local network, wide-area network, theInternet, or any other suitable communication network or networks, andmay be implemented on any suitable platform including wired and/orwireless networks. The clients may communicate with one or more servers13 and/or databases 15. The devices may be directly accessible by theclients 10, 11, or one or more other devices may provide intermediaryaccess such as where a server 13 provides access to resources stored ina database 15. The clients 10, 11 also may access remote platforms 17 orservices provided by remote platforms 17 such as cloud computingarrangements and services. The remote platform 17 may include one ormore servers 13 and/or databases 15. Information from or about a firstclient may be isolated to that client such that, for example,information about client 10 may not be shared with client 11.Alternatively, information from or about a first client may beanonymized prior to being shared with another client. For example, anyclient identification information about client 10 may be removed frominformation provided to client 11 that pertains to client 10.

More generally, various implementations of the presently disclosedsubject matter may include or be implemented in the form ofcomputer-implemented processes and apparatuses for practicing thoseprocesses. Implementations also may be implemented in the form of acomputer program product having computer program code containinginstructions implemented in non-transitory and/or tangible media, suchas floppy diskettes, CD-ROMs, hard drives, USB (universal serial bus)drives, or any other machine readable storage medium, wherein, when thecomputer program code is loaded into and executed by a computer, thecomputer becomes an apparatus for practicing implementations of thedisclosed subject matter. Implementations also may be implemented in theform of computer program code, for example, whether stored in a storagemedium, loaded into and/or executed by a computer, or transmitted oversome transmission medium, such as over electrical wiring or cabling,through fiber optics, or via electromagnetic radiation, wherein when thecomputer program code is loaded into and executed by a computer, thecomputer becomes an apparatus for practicing implementations of thedisclosed subject matter. When implemented on a general-purposemicroprocessor, the computer program code segments configure themicroprocessor to create specific logic circuits. In someconfigurations, a set of computer-readable instructions stored on acomputer-readable storage medium may be implemented by a general-purposeprocessor, which may transform the general-purpose processor or a devicecontaining the general-purpose processor into a special-purpose deviceconfigured to implement or carry out the instructions. Implementationsmay be implemented using hardware that may include a processor, such asa general purpose microprocessor and/or an Application SpecificIntegrated Circuit (ASIC) that implements all or part of the techniquesaccording to implementations of the disclosed subject matter in hardwareand/or firmware. The processor may be coupled to memory, such as RAM,ROM, flash memory, a hard disk or any other device capable of storingelectronic information. The memory may store instructions adapted to beexecuted by the processor to perform the techniques according toimplementations of the disclosed subject matter.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific implementations. However, theillustrative discussions above are not intended to be exhaustive or tolimit implementations of the disclosed subject matter to the preciseforms disclosed. Many modifications and variations are possible in viewof the above teachings. The implementations were chosen and described inorder to explain the principles of implementations of the disclosedsubject matter and their practical applications, to thereby enableothers skilled in the art to utilize those implementations as well asvarious implementations with various modifications as may be suited tothe particular use contemplated.

1. A computer-implemented method for an information discovery systemcomprising: retrieving at least one element of data; extracting at leastone knowledge point from the at least one element of data, wherein theknowledge point comprises an aspect of the at least one element of data;and linking the at least one element of data and the at least oneknowledge point with a traversable link, wherein the at least oneknowledge point is further linked to a second element of data.
 2. Themethod of claim 1, wherein extracting the at least one knowledge pointfurther comprises: determining, using one or more of natural languageprocessing analysis, linguistic analysis, sentiment analysis, andmetadata analysis, the aspect of the at least one element of data. 3.The method of claim 1, wherein the aspect of the at least one element ofdata comprises one item from the group consisting of: an author, apublication date, a source site, a host site, a described industry, adescribed product, a sentiment of intent to purchase, and a sentiment ofcustomer satisfaction.
 4. The method of claim 1, further comprising,during or after extracting the at least one knowledge point, determininga weighting of the traversable link between the at least one knowledgepoint and the at last one element of data.
 5. The method of claim 4,wherein the weighting comprises a confidence level that the aspectcomprising the knowledge point accurately describes the at least oneelement of data.
 6. The method of claim 4, wherein the weighting of thetraversable link between the at least one knowledge point and the atleast one element of data is different than a weighting of a secondtraversable link between the at least one knowledge point and the secondelement of data.
 7. The method of claim 1, wherein the at least oneelement of data and the second element of data both include the aspectcomprising the knowledge point.
 8. The method of claim 1, furthercomprising storing the at least one knowledge point, the at least oneelement of data, and the second element of data, in a knowledge graph.9. The method of claim 8, wherein the at least one element of data isstored as a copy of the at least one element of data from a data sourcefor the at least one element of data or is stored as a link to the atleast one element of data at the original source for the at least oneelement of data.
 10. The method of claim 9, wherein storing a link tothe at least one element of data comprises storing a uniform resourcelocator (URL).
 11. The method of claim 1, wherein the at least oneelement of data is a website posting, a message board posting, or asocial media service posting.
 12. A computer-implemented method for aninformation discovery system comprising: receiving at least one usercriteria from a user device; matching at least one knowledge point tothe at least one user criteria; sending to the user device the least oneknowledge point matching the user criteria and at least one element ofdata linked to the at least one knowledge point; and sending to the userdevice, based on a traversal of a link between the at least one elementof data and at least one additional knowledge point, at least oneadditional element of data linked to the at least one additionalknowledge point.
 13. The method of claim 12, further comprising sendingto the user device at least one additional knowledge point with the atleast one knowledge point and at least one element of data before thetraversal of the link between the at least one element of data and theat least one additional knowledge point.
 14. The method of claim 12,further comprising sending to the user device the at least oneadditional knowledge point after the traversal of the link between theat least one element of data and the at least one additional knowledgepoint.
 15. The method of claim 12, wherein the at least one element ofdata is a website posting, a message board posting, or a social mediaservice posting.
 16. The method of claim 12, wherein the at least oneelement of data is sent to the user device as a copy from a data sourceof the at least one element data.
 17. The method of claim 12, whereinthe at least one element of data is sent to the user device as a URLlinking to the at least one element of data at a data source.
 18. Themethod of claim 12, further comprising: receiving from the user device aselection of the at least one additional element of data; and sending,to the user device, at least a second additional knowledge point linkedto the at least one additional element of data.
 19. The method of claim12, further comprising sending to the user device, with the at least oneknowledge point and at least one element of data, a weight of the linkbetween the at least one knowledge point and the at least one element ofdata.
 20. A system for information discovery, the system comprising: astorage device configured to store a knowledge graph; an informationdiscovery system electronically coupled to the storage and configuredto: retrieve at least one element of data; extract at least oneknowledge point from the at least one element of data, wherein theknowledge point comprises an aspect of the at least one element of data;link the at least one element of data and the at least one knowledgepoint with a traversable link, wherein the at least one knowledge pointis further linked to a second element of data; store the at least oneelement of data, the at least one knowledge point, and the at least oneadditional element of data as a part of the knowledge graph in thestorage; receive at least one user criteria from a user device; matchthe at least one knowledge point to the at least one user criteria; sendto the user device the least one knowledge point matching the usercriteria and the at least one element of data linked to the at least oneknowledge point; and send to the user device, based on a traversal of alink between the at least one element of data and at least oneadditional knowledge point, at least one additional element of datalinked to the at least one additional knowledge point.